CN114115020A - Intelligent control system and control method for height of unmanned aerial vehicle - Google Patents
Intelligent control system and control method for height of unmanned aerial vehicle Download PDFInfo
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- CN114115020A CN114115020A CN202111396123.8A CN202111396123A CN114115020A CN 114115020 A CN114115020 A CN 114115020A CN 202111396123 A CN202111396123 A CN 202111396123A CN 114115020 A CN114115020 A CN 114115020A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25257—Microcontroller
Abstract
The invention discloses an intelligent control system and a control method for the height of an unmanned aerial vehicle, relates to the technical field of unmanned aerial vehicle control, and solves the technical problems that when the unmanned aerial vehicle is adjusted in the existing scheme, influence factors are not comprehensively considered, so that the unmanned aerial vehicle is unreasonably adjusted, and the operation safety of the unmanned aerial vehicle cannot be ensured; the system comprises a processor, a management monitoring module, a climate monitoring module, a data storage module, an execution control module, an adjustment analysis module and an instruction analysis module; the invention is provided with the climate monitoring module and the adjustment analysis module, so that factors influencing the flight of the unmanned aerial vehicle are considered and analyzed to obtain a predicted track, the unmanned aerial vehicle is adjusted according to the predicted track, the reasonability of height adjustment of the unmanned aerial vehicle is ensured, and the unmanned aerial vehicle can be safely adjusted and operated; the predicted tracks correspond to the acquisition areas, and when the unmanned aerial vehicle flies away or is about to fly away from the current acquisition area, the predicted tracks are acquired again, so that the reasonable continuity of the acquired predicted tracks is guaranteed.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle control, relates to an intelligent control technology, and particularly relates to an unmanned aerial vehicle height intelligent control system and a control method thereof.
Background
An intelligent unmanned aerial vehicle (unmanned aerial vehicle) is used for controlling various tasks such as monitoring, flight performance, special flight and the like. The control system of the unmanned aerial vehicle is the brain of the unmanned aerial vehicle, constantly controls the speed and the attitude of the unmanned aerial vehicle, and plays an important role in reliable flight of the unmanned aerial vehicle. The altitude control of unmanned aerial vehicle realizes at unmanned aerial vehicle's speed and pitch angle, and along with the constantly change of height, unmanned aerial vehicle's speed and pitch angle also should change constantly, and the utility of executor also can change along with the change of flying speed.
The invention patent with publication number CN110667847A discloses an intelligent flying height control platform, which comprises: the layout heuristic equipment is arranged on the unmanned aerial vehicle and used for identifying a plurality of cloud objects in a front airspace image based on cloud imaging characteristics and uniformly dividing the front airspace image in the horizontal direction to obtain an upper image area, a middle image area and a lower image area; and the content detection equipment is connected with the layout heuristic equipment and is used for counting the number of cloud objects in each of the upper image area, the middle image area and the lower image area and sending a height lifting signal when the number of clouds in the upper image area is minimum.
The intelligent flying height control platform in the scheme has stable operation, safety and reliability, and can automatically control the height of the unmanned aerial vehicle according to the detection result of the upper, middle and lower three-stage distribution density of the cloud in front of the unmanned aerial vehicle, so that the flying safety performance of the unmanned aerial vehicle is ensured; however, above-mentioned scheme is after according to cloud monitoring result adjustment height, can not guarantee that image, video etc. that unmanned aerial vehicle obtained can satisfy the requirement, does not consider other factors that influence unmanned aerial vehicle flight, leads to unmanned aerial vehicle's altitude mixture control too simple crudely.
Disclosure of Invention
The invention provides an unmanned aerial vehicle height intelligent control system and a control method thereof, which are used for solving the technical problems that when an unmanned aerial vehicle is adjusted in the existing scheme, influence factors are not considered comprehensively, so that the unmanned aerial vehicle is adjusted unreasonably and the unmanned aerial vehicle cannot be ensured to run safely.
The purpose of the invention can be realized by the following technical scheme: an unmanned aerial vehicle height intelligent control system comprises a processor and a data storage module;
the processor is respectively communicated and/or electrically connected with the instruction analysis module, the adjustment analysis module, the data storage module and the execution control module, and the adjustment analysis module is respectively communicated and/or electrically connected with the instruction analysis module and the execution control module;
the instruction analysis module analyzes the task instruction, generates target data according to an analysis result and sends the target data to the adjustment analysis module;
the adjustment analysis module acquires target data, acquires image data through an acquisition sensor carried by the unmanned aerial vehicle, comprehensively analyzes the target data, the image data and the climate characterization label to generate a predicted track, and sends the predicted track to the execution control module; wherein the climate characterization tag is obtained through climate data;
the execution control module adjusts the unmanned aerial vehicle according to the predicted track, and generates a predicted track chain after the adjustment is completed.
Preferably, the acquisition sensor comprises a plurality of high-definition cameras, a temperature sensor, a humidity sensor, an air pressure sensor and an air speed sensor; the image data are a plurality of environment images around the unmanned aerial vehicle flight process acquired by the high-definition camera, and the climate representation tag is acquired by the climate monitoring module.
Preferably, the target data is obtained according to the task instruction, and the target data is in a data sequence format, specifically: [ flight mode, initial position, target position, maximum velocity, maximum acceleration ]; the flight mode is set to be 0 or 1, when the flight mode is set to be 0, the flight mode is represented as manual flight, and when the flight mode is set to be 1, the flight mode is represented as automatic flight; the format of the initial position and the target position is geographic coordinates.
Preferably, the generating of the predicted trajectory comprises:
when the climate characterization label is greater than or equal to the characterization label threshold value, judging that the environment where the unmanned aerial vehicle is located is abnormal, and sending an environment abnormity early warning signal to the intelligent terminal; wherein the token tag threshold is an integer greater than 0;
when the climate characterization label is smaller than the characterization label threshold value, analyzing the image data through an image processing technology, and when obstacles exist around the unmanned aerial vehicle, sending an obstacle early warning signal to the intelligent terminal;
when no obstacle exists around the unmanned aerial vehicle, planning a path in the acquisition area according to the state of the unmanned aerial vehicle and target data, and marking the path as a predicted track; the acquisition area is an effective area of acquired environment data and image data, and when the unmanned aerial vehicle flies away from the current acquisition area, the predicted track is acquired again.
Preferably, adjusting the drone according to the predicted trajectory includes:
when the execution control module receives the predicted track, extracting a flight mode in the target data;
when the flight mode is 0, the predicted track is sent to the intelligent terminal, the user controls the unmanned aerial vehicle according to the predicted track, and when the unmanned aerial vehicle deviates from the predicted track, a track deviation signal is sent to the intelligent terminal in time;
when the flight mode is 1, automatically adjusting the unmanned aerial vehicle according to the predicted track through the execution control module;
when the automatic adjustment is completed, generating a predicted trajectory chain; the predicted track chain comprises time, target data, image data, a climate characterization label, an acquisition area and a predicted track.
Preferably, the processor is further in communication and/or electrical connection with a management monitoring module and a climate monitoring module, the data storage module is in communication and/or electrical connection with the climate monitoring module and the execution control module respectively, and the management monitoring module is in communication and/or electrical connection with the instruction analysis module;
the management monitoring module is in communication and/or electrical connection with an intelligent terminal of a user; a user sends a task instruction to the management monitoring module through the intelligent terminal; the intelligent terminal comprises an intelligent mobile phone and a notebook computer, the task instruction comprises a flight mode, an initial position, a target position, a maximum speed and a maximum acceleration, and the flight mode comprises automatic flight and manual flight;
the data storage module is used for storing data.
Preferably, the climate monitoring module is configured to obtain a climate characterization tag, and includes:
acquiring climate training data through a data storage module; the weather training data comprises environmental data when the unmanned aerial vehicle breaks down, environmental data of normal flight of the unmanned aerial vehicle and a weather representation label corresponding to the environmental data, the weather representation label is an integer larger than 0, and the larger the weather representation label is, the larger the probability of the unmanned aerial vehicle breaking down is;
after the climate training data are normalized, training, testing and verifying the artificial intelligence model, and marking the trained artificial intelligence model as a climate evaluation model;
acquiring environmental data of an environment where the unmanned aerial vehicle is located in real time through an acquisition sensor; wherein the environmental data includes temperature, humidity, air pressure, and wind speed;
the environmental data are normalized and then input to a climate evaluation module to obtain an output result, the output result is subjected to inverse normalization processing to obtain a climate representation label corresponding to the environmental data, and the climate representation label corresponding to the real-time environmental data is sent to an adjustment analysis module.
An unmanned aerial vehicle height intelligent control method comprises the following steps:
the method comprises the following steps: the instruction analysis module analyzes the task instruction, generates target data according to an analysis result and sends the target data to the adjustment analysis module;
step two: the adjustment analysis module acquires target data and image data, comprehensively analyzes the target data, the image data and the climate characterization label to generate a predicted track, and sends the predicted track to the execution control module;
step three: and the execution control module adjusts the unmanned aerial vehicle according to the predicted track, and generates a predicted track chain after the adjustment is finished.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention is provided with a climate monitoring module and an adjusting and analyzing module; the climate monitoring module acquires a climate representation label corresponding to real-time environment data through an artificial intelligence model and the real-time environment data of unmanned aerial vehicle flight, the adjustment analysis module acquires target data, meanwhile, the acquisition sensor carried by the unmanned aerial vehicle acquires image data, the target data, the image data and the climate representation label are comprehensively analyzed to generate a prediction track, and the prediction track is sent to the execution control module; the setting of climate monitoring module and regulation analysis module will influence the factor of unmanned aerial vehicle flight and be familiar with the consideration to data analysis obtains the prediction orbit, adjusts unmanned aerial vehicle according to the prediction orbit, guarantees unmanned aerial vehicle altitude mixture control's rationality, ensures that unmanned aerial vehicle can the safety regulation and operate.
2. Target data is obtained through a task instruction, and the target data is in a data sequence format; the content that includes in the target data can set up according to concrete requirement, can satisfy unmanned aerial vehicle height or position control under different users, the different flight environment, has expanded the range of application of this application.
3. The predicted tracks in the invention all correspond to an acquisition area; the collection area is an effective area of collected environment data and image data, and when the unmanned aerial vehicle flies away or is about to fly away from the current collection area, the predicted track is obtained again, so that the reasonable continuity of obtaining the predicted track is guaranteed.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the principles of the present invention;
FIG. 2 is a schematic diagram of the steps of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used herein is for the purpose of describing embodiments and is not intended to be limiting and/or limiting of the present disclosure; it should be noted that the singular forms "a," "an," and "the" include the plural forms as well, unless the context clearly indicates otherwise; also, although the terms first, second, etc. may be used herein to describe various elements, the elements are not limited by these terms, which are only used to distinguish one element from another.
Referring to fig. 1-2, an intelligent control system for the altitude of an unmanned aerial vehicle includes a processor and a data storage module;
the processor is respectively communicated and/or electrically connected with the instruction analysis module, the adjustment analysis module, the data storage module and the execution control module, and the adjustment analysis module is respectively communicated and/or electrically connected with the instruction analysis module and the execution control module;
the instruction analysis module analyzes the task instruction, generates target data according to an analysis result and sends the target data to the adjustment analysis module;
the adjustment analysis module acquires target data, simultaneously acquires image data through an acquisition sensor carried by the unmanned aerial vehicle, comprehensively analyzes the target data, the image data and the climate characterization label to generate a predicted track, and sends the predicted track to the execution control module; the climate characterization label is obtained through climate data;
and the execution control module adjusts the unmanned aerial vehicle according to the predicted track, and generates a predicted track chain after the adjustment is finished.
The acquisition sensor comprises a plurality of high-definition cameras, a temperature sensor, a humidity sensor, an air pressure sensor and an air speed sensor; the image data is an environment image of the periphery of the unmanned aerial vehicle in the flying process, which is acquired through a plurality of high-definition cameras, and the climate representation tag is acquired through a climate monitoring module; in other preferred embodiments, the collecting sensor further comprises a noise sensor and a resistance sensor; aiming at different flight environments, different acquisition sensors can be configured for the unmanned aerial vehicle, and the environment around the unmanned aerial vehicle can be timely acquired.
In the embodiment, the acquisition sensor comprises a sensor for monitoring the flight environment and a high-definition camera for acquiring the surrounding environment in the flight process, and data acquired by the acquisition sensor is used as a data basis for predicting the flight trajectory of the unmanned aerial vehicle; factors influencing the flight of the unmanned aerial vehicle are comprehensively considered, and the comprehensive analysis can ensure the reasonable and accurate prediction of the flight track of the unmanned aerial vehicle.
Target data is obtained according to the task instruction, and the target data is in a data sequence format; analyzing the task instruction, and then converting the target data into a data sequence format through a mathematical method; such as: [ flight mode, initial position, target position, maximum velocity, maximum acceleration ]; the flight mode is set to be 0 or 1, when the flight mode is set to be 0, the flight mode is represented as manual flight, and when the flight mode is set to be 1, the flight mode is represented as automatic flight; the format of the initial position and the target position is geographic coordinates.
In the embodiment, the task instruction is analyzed, and the task instruction is converted into target data in a data sequence form; the content included in the target data can be set according to specific requirements, if the inclination angle of the unmanned aerial vehicle is limited as required, the target data is set to be [ a flight mode, an initial position, a target position, a maximum speed, a maximum acceleration and an inclination angle ], and the task instruction is flexibly converted according to the requirements, so that the adjustment of the height or the track of the unmanned aerial vehicle under different conditions can be met.
Acquiring climate training data through a data storage module; in the scheme, the climate training data can be historical experience data, namely data acquired by the unmanned aerial vehicle during previous flight, and can also be flight simulation data, namely data acquired by configuring various flight environments during the simulated flight of the unmanned aerial vehicle; the climate training data comprises environmental data when the unmanned aerial vehicle breaks down, environmental data of normal flight of the unmanned aerial vehicle and a climate representation label corresponding to the environmental data, the climate representation label is an integer larger than 0, and the greater the climate representation label is, the greater the probability of the unmanned aerial vehicle breaking down is represented;
after the climate training data are normalized, training, testing and verifying the artificial intelligence model, and marking the trained artificial intelligence model as a climate evaluation model; the model in the scheme can be a single model or a fusion model formed by fusing a plurality of models, and is specifically selected according to the prediction precision;
the environment data of the environment where the unmanned aerial vehicle is located are acquired in real time through the acquisition sensor, the environment data are input to the climate evaluation module after being normalized to acquire an output result, the output result is subjected to reverse normalization processing to acquire a climate representation label corresponding to the environment data, and the climate representation label corresponding to the real-time environment data is sent to the adjustment analysis module.
In the embodiment, the climate representation label is obtained by combining the artificial intelligence model with the climate training data; the characteristics of high robustness and nonlinear fitting of the artificial intelligence model are fully utilized, so that the influence of abnormal data can be avoided, and the prediction precision and the prediction efficiency of the climate characterization label can be improved.
When the climate characterization label is greater than or equal to the characterization label threshold value, judging that the environment where the unmanned aerial vehicle is located is abnormal, and sending an environment abnormity early warning signal to the intelligent terminal; if the climate characterization label is 2 and the threshold value of the characterization label is 1, judging that the environment where the unmanned aerial vehicle is located is abnormal;
when the climate characterization label is smaller than the characterization label threshold value, analyzing the image data through an image processing technology, and when obstacles exist around the unmanned aerial vehicle, sending an obstacle early warning signal to the intelligent terminal;
when no obstacle exists around the unmanned aerial vehicle, planning a path in the acquisition area according to the state of the unmanned aerial vehicle and target data, and marking the path as a predicted track; the collection area is an effective area of collected environment data and image data, and when the unmanned aerial vehicle flies away from the current collection area, the predicted track is obtained again.
In the embodiment, the predicted track is generated by sequentially analyzing the climate representation label, the image data, the unmanned aerial vehicle state and the target data; the unmanned aerial vehicle state evaluation system comprises a climate representation tag, an unmanned aerial vehicle state evaluation module, a target data analysis module, a trajectory planning model and a data analysis module, wherein the climate representation tag is used for analyzing whether the flight environment of the unmanned aerial vehicle meets the requirement of height adjustment or not, image data is used for analyzing whether obstacles exist around the unmanned aerial vehicle or not, the unmanned aerial vehicle state is used for evaluating whether the unmanned aerial vehicle can meet the requirement of height change or trajectory change or not, and when the conditions are met, the target data and the existing trajectory planning model are combined to generate a predicted trajectory of the unmanned aerial vehicle; the influence factor to unmanned aerial vehicle altitude mixture promotion or orbit change is considered comprehensively, guarantees that unmanned aerial vehicle's transform can go on under reliable environment, and flies to leave current collection area when unmanned aerial vehicle, in some embodiments, can be when unmanned aerial vehicle is about to fly to leave current collection area, reacquires the prediction orbit, guarantees the reasonable continuation of orbit prediction.
When the execution control module receives the predicted track, extracting a flight mode in the target data;
when the flight mode is 0, the predicted track is sent to the intelligent terminal, the user controls the unmanned aerial vehicle according to the predicted track, and when the unmanned aerial vehicle deviates from the predicted track, a track deviation signal is sent to the intelligent terminal in time;
when the flight mode is 1, automatically adjusting the unmanned aerial vehicle according to the predicted track through the execution control module;
when the automatic adjustment is completed, generating a predicted trajectory chain; the predicted track chain comprises time, target data, image data, a climate representation label, an acquisition area and a predicted track.
In the embodiment, the unmanned aerial vehicle is adjusted by combining the flight mode and the predicted track; when the flight mode is 0, the predicted track is sent to the intelligent terminal, the user automatically adjusts the height or the direction of the unmanned aerial vehicle according to the predicted track, and when the unmanned aerial vehicle deviates from the predicted track, the user is reminded; when the flight mode is 1, the unmanned aerial vehicle is intelligently adjusted through the execution control module; and after the adjustment is completed, generating a prediction track chain for archiving and filing; the adjusting process can meet the requirements of different users and different scenes, an evidence chain is reserved, and when problems occur, the adjusting process can be quickly positioned and adjusted.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
acquiring environmental data and image data through an acquisition sensor carried by an unmanned aerial vehicle; acquiring climate training data through a data storage module, normalizing the climate training data, training, testing and verifying the artificial intelligence model, and marking the trained artificial intelligence model as a climate evaluation model; the environment data of the environment where the unmanned aerial vehicle is located are acquired in real time through the acquisition sensor, the environment data are input to the climate evaluation module after being normalized to acquire an output result, the output result is subjected to reverse normalization processing to acquire a climate representation label corresponding to the environment data, and the climate representation label corresponding to the real-time environment data is sent to the adjustment analysis module.
The instruction analysis module analyzes the task instruction, generates target data according to an analysis result and sends the target data to the adjustment analysis module; the adjustment analysis module obtains target data, simultaneously, obtains image data through the collection sensor that unmanned aerial vehicle carried on, to target data, image data and the comprehensive analysis of climatic representation label generation prediction orbit, when unmanned aerial vehicle fly away or be about to fly away from current collection area, reacquire the prediction orbit, will predict the orbit and send to execution control module.
When the execution control module receives the predicted track, extracting a flight mode in the target data; when the flight mode is 0, the predicted track is sent to the intelligent terminal, the user controls the unmanned aerial vehicle according to the predicted track, and when the unmanned aerial vehicle deviates from the predicted track, a track deviation signal is sent to the intelligent terminal in time; when the flight mode is 1, automatically adjusting the unmanned aerial vehicle according to the predicted track through the execution control module; when the auto-tuning is complete, a chain of predicted trajectories is generated.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (7)
1. An unmanned aerial vehicle height intelligent control system comprises a processor and a data storage module, and is characterized in that the processor is respectively in communication and/or electrical connection with an instruction analysis module, an adjustment analysis module, a data storage module and an execution control module;
the instruction analysis module analyzes the task instruction, generates target data according to an analysis result and sends the target data to the adjustment analysis module;
the adjustment analysis module acquires target data, acquires image data through an acquisition sensor carried by the unmanned aerial vehicle, comprehensively analyzes the target data, the image data and the climate characterization label to generate a predicted track, and sends the predicted track to the execution control module; wherein the climate characterization tag is obtained through climate data;
the execution control module adjusts the unmanned aerial vehicle according to the predicted track, and generates a predicted track chain after the adjustment is completed.
2. The unmanned aerial vehicle highly intelligent control system of claim 1, wherein the image data is an image of an environment around the unmanned aerial vehicle during flight acquired by a plurality of high-definition cameras, and the climate characterization tag is acquired by a climate monitoring module.
3. The unmanned aerial vehicle highly intelligent control system of claim 1, wherein the target data is obtained according to a task instruction, and the target data is in a data sequence format.
4. The unmanned aerial vehicle highly intelligent control system of claim 2, wherein the climate monitoring module is configured to obtain a climate characterization tag, and comprises:
acquiring climate training data through a data storage module; the climate training data comprises environmental data when the unmanned aerial vehicle breaks down, environmental data of normal flight of the unmanned aerial vehicle and a climate representation label corresponding to the environmental data;
after the climate training data are normalized, training, testing and verifying the artificial intelligence model, and marking the trained artificial intelligence model as a climate evaluation model;
the environment data of the environment where the unmanned aerial vehicle is located are acquired in real time through the acquisition sensor, the environment data are input to the climate evaluation module after being normalized to acquire an output result, the output result is subjected to reverse normalization processing to acquire a climate representation label corresponding to the environment data, and the climate representation label corresponding to the real-time environment data is sent to the adjustment analysis module.
5. The unmanned aerial vehicle height intelligent control system of claim 4, wherein when the climate characterization tag is greater than or equal to the characterization tag threshold, it is determined that the unmanned aerial vehicle is in an abnormal environment, and an environmental abnormality early warning signal is sent to the intelligent terminal;
when the climate characterization label is smaller than the characterization label threshold value, analyzing the image data through an image processing technology, and when obstacles exist around the unmanned aerial vehicle, sending an obstacle early warning signal to the intelligent terminal;
when no obstacle exists around the unmanned aerial vehicle, planning a path in the acquisition area according to the state of the unmanned aerial vehicle and target data, and marking the path as a predicted track; the acquisition area is an effective area of acquired environment data and image data, and when the unmanned aerial vehicle is about to fly away from the current acquisition area, the predicted track is acquired again.
6. The intelligent drone altitude control system of claim 5, wherein adjusting the drone according to the predicted trajectory includes:
when the execution control module receives the predicted track, extracting a flight mode in the target data;
when the flight mode is 0, the predicted track is sent to the intelligent terminal, the user controls the unmanned aerial vehicle according to the predicted track, and when the unmanned aerial vehicle deviates from the predicted track, a track deviation signal is sent to the intelligent terminal in time;
when the flight mode is 1, automatically adjusting the unmanned aerial vehicle according to the predicted track through the execution control module;
when the automatic adjustment is completed, generating a predicted trajectory chain; the predicted track chain comprises time, target data, image data, a climate characterization label, an acquisition area and a predicted track.
7. An unmanned aerial vehicle height intelligent control method is characterized by comprising the following steps:
the method comprises the following steps: the instruction analysis module analyzes the task instruction, generates target data according to an analysis result and sends the target data to the adjustment analysis module;
step two: the adjustment analysis module acquires target data and image data, comprehensively analyzes the target data, the image data and the climate characterization label to generate a predicted track, and sends the predicted track to the execution control module;
step three: and the execution control module adjusts the unmanned aerial vehicle according to the predicted track, and generates a predicted track chain after the adjustment is finished.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114600658A (en) * | 2022-03-22 | 2022-06-10 | 广东电网有限责任公司 | Branch cutting device based on unmanned aerial vehicle |
| CN116301034A (en) * | 2023-02-24 | 2023-06-23 | 哈尔滨数字律动科技有限公司 | Unmanned aerial vehicle monitoring system and method based on radio communication technology |
| CN117406818A (en) * | 2023-12-15 | 2024-01-16 | 南京中鑫智电科技有限公司 | Power distribution room environment adjusting method and system based on track robot |
-
2021
- 2021-11-23 CN CN202111396123.8A patent/CN114115020A/en not_active Withdrawn
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114600658A (en) * | 2022-03-22 | 2022-06-10 | 广东电网有限责任公司 | Branch cutting device based on unmanned aerial vehicle |
| CN116301034A (en) * | 2023-02-24 | 2023-06-23 | 哈尔滨数字律动科技有限公司 | Unmanned aerial vehicle monitoring system and method based on radio communication technology |
| CN116301034B (en) * | 2023-02-24 | 2023-09-15 | 哈尔滨数字律动科技有限公司 | Unmanned aerial vehicle monitoring system and method based on radio communication technology |
| CN117406818A (en) * | 2023-12-15 | 2024-01-16 | 南京中鑫智电科技有限公司 | Power distribution room environment adjusting method and system based on track robot |
| CN117406818B (en) * | 2023-12-15 | 2024-03-08 | 南京中鑫智电科技有限公司 | Power distribution room environment adjusting method and system based on track robot |
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